Tourism demand forecasting under conceptual drift during COVID-19: an ensemble deep learning model-Reference-Cited by-同舟云学术

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Tourism demand forecasting under conceptual drift during COVID-19: an ensemble deep learning model

Published:2023-10-25 Issue: Volume: Page:1-20
ISSN:1368-3500
Container-title:Current Issues in Tourism
language:en
Short-container-title:Current Issues in Tourism

Author:

Bi Jian-Wu¹,Han Tian-Yu¹,Yao Yanbo¹,Yang Tao¹

Affiliation:

1. College of Tourism and Service Management, Nankai University, Tianjin, People’s Republic of China

Funder

National Natural Science Foundation of China

Liberal Arts Development Fund of Nankai University

One Hundred Talents Program of Nankai University

Publisher

Informa UK Limited

Subject

Tourism, Leisure and Hospitality Management,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/13683500.2023.2273922

Reference37 articles.

1. Probabilistic Forecasts Using Expert Judgment: The Road to Recovery From COVID-19

2. Forecasting daily tourism demand for tourist attractions with big data: An ensemble deep learning method;Bi J. W.;Journal of Travel Research,2022

3. Tourism demand forecasting with time series imaging: A deep learning model

4. Daily tourism volume forecasting for tourist attractions

5. Modelling the interdependence of tourism demand: The global vector autoregressive approach

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1. A Sustainable Rental Price Prediction Model Based on Multimodal Input and Deep Learning—Evidence from Airbnb;Sustainability;2024-07-25

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